An antifraud solution for a company: requirements for data, integration and security

Data requirements

The accuracy of an anti-fraud system depends directly on the quality and completeness of the input data. Without correctly structured information, even the most advanced machine learning algorithms and scoring models will not be able to detect fraudulent operations with sufficient reliability. Before implementing a solution, it is important to understand what data the system will process, where to obtain it, and what quality criteria it must meet.

Types and formats of input data

Anti-fraud systems analyze several categories of data, each of which addresses its own tasks in the risk assessment process.

In terms of formats, antifraud solutions accept data in several forms:

• JSON and XML — standard formats for exchanging data via an API in real time;
• CSV and TSV — used for batch loading of historical data, reference directories, blacklists;
• Base64 — for transmitting binary data, including document images and biometric snapshots;
• Protobuf — used in high-load systems where serialization speed is critical.

It is important to ensure uniformity of formats at all integration points. If dates are recorded as DD/MM/YYYY in one system and MM/DD/YYYY in another, this will lead to analytics errors and false positives.

Data sources for analysis

The effectiveness of an anti-fraud system grows with the number of sources it is able to aggregate. The broader the data coverage, the more accurately the scoring models detect fraudulent patterns.

Internal sources are the data a company accumulates on its own:

• CRM and accounting systems — customer information, interaction history, segmentation;
• Payment gateway — transaction details, operation statuses, data on refunds and chargebacks;
• Logging systems — authorization events, actions in the personal account, error logs;
• Call center — records of inquiries, complaints, results of manual checks.

External sources expand the analysis capabilities with data unavailable within the company’s perimeter:

• Geolocation services determine the real location by IP address, detect the use of proxies and VPNs, and reveal “impossible travel” — when a single account appears in geographically distant locations within a short time;
• Reputation databases contain lists of compromised IP addresses, known fraudulent emails and phone numbers, and registries of stolen payment cards;
• Credit history bureaus (NBKI, OKB, Equifax) provide information about a customer’s credit load and payment discipline;
• Government registries make it possible to verify documents, check the validity of passports, the status of an INN (taxpayer number), and inclusion in lists of bankrupts or debtors;
• Sanctions and AML databases — international and national lists of persons subject to restrictive measures.

Consortium data sharing deserves special attention, when several organizations in the same industry share information about detected fraudsters. This approach is especially effective in the financial sector and e-commerce: if an attacker committed fraud at one company, other members of the consortium learn about it and can prevent similar attempts.

When connecting external sources, it is important to take into account the requirements of personal data legislation. Transferring information to third parties is permitted only when there is a legal basis — the consent of the subject, a contract, or the operator’s legitimate interest.

Minimum volume and quality of data

An anti-fraud system will not start working effectively from day one — it needs to accumulate statistics to train the models. The minimum volume of data depends on the specifics of the business and the type of tasks being solved.

To launch basic rules, historical data covering 3–6 months is enough. This is sufficient to determine typical customer behavior patterns and identify obvious deviations. However, for machine learning models to work fully, an array of several tens of thousands of transactions is required, with labeled examples of fraudulent operations. The “fraud / legitimate transactions” ratio in the training sample is critical for the model’s quality: if there are too few fraud cases, the algorithm will not be able to identify patterns.

Data quality is determined by several parameters:

• Completeness — the absence of missing values in mandatory fields. If the IP address is not filled in for 30% of transactions, the system will not be able to correctly assess geolocation risks.
• Currency — the data must reflect the current state. Outdated blacklists or out-of-date directories of issuing banks reduce detection accuracy.
• Accuracy — the correspondence of the data to reality. Incorrectly recognized documents, manual-entry errors, and duplicate records distort analytics and lead to false positives.
• Consistency — uniformity of data across different systems. If a customer is listed as “Ivanov I.I.” in one source and “IVANOV IVAN IVANOVICH” in another, the system may fail to link these records.
• Timeliness — the data must reach the system with minimal delay. For online scoring, processing a transaction in fractions of a second is critical; a lag in information about previous operations reduces assessment accuracy.

In practice, data quality is maintained through Data Quality Management procedures: regular deduplication, format validation at the intake stage, enrichment of records from external sources. Before launching an anti-fraud system, it is recommended to audit existing data and eliminate critical problems — this helps avoid situations where the system blocks legitimate customers due to incorrect information in the database.

It is important to remember: anti-fraud is not a static tool but a continuous process. The system must constantly learn from new data in order to recognize fresh fraud schemes. Regular labeling of detected cases and feedback from the fraud analytics team improve the models’ accuracy over time.

Integration requirements

The effectiveness of an anti-fraud system depends directly on how organically it fits into the company’s existing infrastructure. Even the most accurate detection algorithms will prove useless if the system cannot exchange data with the payment gateway or receive transaction information in real time. That is why, when choosing a solution, it is critically important to assess the available connection methods, compatibility with the systems already in use, and realistic implementation timeframes.

Connection methods: API, SDK, connectors

Modern antifraud solutions offer several integration mechanisms, each suited to specific scenarios.

The REST API remains the main and most universal method. It makes it possible to exchange data between any systems over the standard HTTP protocol. For web services and server applications, this is the optimal choice: a transaction check request is sent to the provider’s endpoint, and the response with the verdict comes back in JSON format. A well-documented API allows technical specialists to complete a basic integration in a few hours — this is effectively comparable to adding an analytics counter to a website.

An SDK (Software Development Kit) is intended for mobile applications and scenarios that require deep integration at the device level. A mobile SDK for Android and iOS collects device parameters, analyzes the user’s behavioral patterns, and transmits this data to the anti-fraud system’s server. This approach makes it possible to detect emulators, rooted devices, signs of remote control, and other indicators of potential fraud that are unavailable when only server-side data is analyzed.

A JavaScript module for web applications works on a similar principle: the script is embedded on the site’s pages and collects information about the browser, the session, and the specifics of the user’s interaction with the interface. If a visitor comes through a regular browser on a mobile device, a separate SDK is not required — a JavaScript integration is enough.

Ready-made connectors simplify connecting to common business systems and platforms. Connectors for popular CMSs, payment gateways and e-commerce platforms reduce integration time to a minimum, since the main interaction logic is already implemented. However, non-standard configurations will require the development of custom integration modules.

Compatibility with corporate systems

An antifraud solution does not exist in isolation — it must exchange data with CRM, ERP, processing systems, customer databases and other components of the corporate infrastructure.

Integration with a CRM system makes it possible to enrich transaction data with customer information: purchase history, registration date, loyalty level, previous incidents. This information improves scoring accuracy and reduces the number of false positives for regular customers.

A link with an ERP provides access to data on orders, stock levels, and logistics. For retail, this is critically important: an abnormally large order of a scarce product with delivery to an atypical region is a signal for an additional check.

Processing and payment systems are the key sources of transactional data. Anti-fraud must receive payment information (amount, card, IP address, device) and return a decision: approve, decline, or request additional verification. For real-time operation, response speed is critical — enterprise-class systems process a request in 50–100 milliseconds.

To ensure compatibility, several technical details should be clarified. Does the solution support the data formats used in the company (JSON, XML, specific protocols)? Are there ready-made integrations with the platforms in use? Is two-way data synchronization possible? How is authorization between systems organized (OAuth 2.0, API keys, certificates)?

A separate question is the deployment option. Cloud SaaS solutions are easier to launch, but transmitting data externally is not suitable for every organization. An on-premise installation on your own servers gives full control over the data but requires dedicated infrastructure and support resources. Hybrid schemes make it possible to store sensitive data locally while performing computationally intensive tasks in the cloud.

Implementation stages and timeframes

Implementing an anti-fraud system is a project with clearly defined stages. An attempt to rush the timeline or skip stages usually results in problems at launch.

Realistic expectations for timeframes: a minimal implementation with a basic API integration — from two to four weeks; a full-fledged mid-scale project — two to three months; a comprehensive enterprise implementation — from four to six months. A vendor who promises to launch a complex system “in a single day” is either simplifying the architecture to an unacceptable level or shifting the integration work onto the customer.

When estimating timeframes, internal factors should also be taken into account: the availability of technical specialists, the time for internal approvals, and dependencies on the release cycles of other products. Practice shows that organizational delays often exceed technical ones.

Security requirements

An anti-fraud system handles data whose leakage can cause a company not only reputational but also direct financial damage. Payment details, users’ behavioral profiles, check results — all of this requires multi-level protection. Without a well-built security architecture, an antifraud solution turns from a protection tool into a potential point of vulnerability.

Protecting data in transit and at rest

Information circulating between the components of an anti-fraud system must be protected at all stages of its lifecycle: during collection, transmission, processing and storage.

For data transmission, the industry standard is the TLS protocol, version 1.2 or 1.3. Earlier versions contain known vulnerabilities and should not be used in a production environment. TLS 1.3 provides not only traffic encryption but also authentication of the parties, protection against man-in-the-middle attacks, and a guarantee of the integrity of the transmitted messages. For API integrations, it is additionally recommended to use mutual authentication (mTLS), in which both the client and the server confirm their authenticity with certificates.

To encrypt data at rest, the AES algorithm with a 256-bit key length is used. This standard is recognized as sufficiently strong to protect confidential information and is used in the financial sector, government systems and critical infrastructure. Encryption must cover databases with customer profiles, transaction logs, check results and backups.

Cryptographic key management deserves special attention. Keys must not be stored together with the encrypted data. The optimal approach is to use hardware security modules (HSM) or specialized key management services that support rotation and access auditing. Regular key rotation reduces the risks in case of their compromise.

Compliance with Federal Law 152-FZ, GDPR, PCI DSS

An anti-fraud system inevitably processes personal data and often payment information. This imposes obligations to comply with several regulatory regimes at once.

When choosing an anti-fraud platform, it is important to clarify whether the provider has up-to-date compliance certificates and is ready to provide documentation for the company’s own audit.

Access control and event logging

Even securely encrypted data is vulnerable if an unlimited circle of employees has access to it. The principle of least privilege is the foundation of secure work with an anti-fraud system.

A role-based access model (RBAC) makes it possible to delimit authority according to functional responsibilities. An analyst sees aggregated statistics and can configure detection rules but has no access to customers’ personal data. A support operator works with specific incidents but cannot change the system configuration. An administrator manages technical parameters, but actions that change critical settings require confirmation from a second person.

Multi-factor authentication (MFA) must be mandatory for all users with administrative privileges and is recommended for the rest. The combination of a password and a one-time code from an authenticator app significantly reduces the risk of unauthorized access even if credentials are compromised.

Logging of security events is not an option but a mandatory component. The system must record all authentication attempts (successful and unsuccessful), configuration changes, actions with user data, anti-fraud rule triggers, and administrators’ actions. Logs must be protected from modification and deletion, including by privileged users. The best practice is to transmit logs to a separate SIEM system in real time.

The log retention period is determined both by internal policies and by regulatory requirements. For financial organizations, the minimum period is usually 5 years. Logs must make it possible to retrospectively investigate incidents and confirm compliance with regulatory requirements during inspections.

Regular audits of access rights and log analysis make it possible to detect anomalies: atypical login times, mass data requests, privilege escalation attempts. Integrating the anti-fraud system with corporate security monitoring tools turns it from an isolated tool into an element of a unified protection ecosystem.